Python Powerhouse Gen AI From Basics to Advanced Programming

Goals

Section 1: Lists and Tuples in Python

Begin your journey by understanding the foundational data structures in Python—lists and tuples. You'll explore how to create, manipulate, and utilize these structures effectively. Through practical exercises and case studies, you'll learn to perform operations such as sorting, indexing, and slicing lists, as well as understand the immutable nature of tuples. This section provides a solid grounding in Python's core data types, essential for any advanced programming tasks.

Section 2: Sets and Dictionaries in Python

Delve into more advanced data structures: sets and dictionaries. Learn to define and manipulate sets, handle various operations like union, intersection, and difference, and manage dictionaries with key-value pairs. This section equips you with the skills to handle more complex data scenarios and optimize your data management strategies.

Section 3: Date and Time Manipulations

Master the art of handling date and time data with Python. You'll learn how to format and manipulate date-time objects, extract meaningful components, and apply these skills in real-world data scenarios. This section includes a case study on customer churn prediction, where you’ll practice data preprocessing and feature engineering techniques crucial for accurate data analysis.

Section 4: Functional Programming

Explore functional programming paradigms with Python, focusing on lambda functions, map, reduce, and filter operations. Understand how to apply these concepts to simplify code and perform efficient data transformations. This section provides a deep dive into functional programming techniques, offering practical examples and applications.

Section 5: Advanced Python Programming

Expand your Python expertise with advanced programming concepts such as recursion, feature engineering, and data analysis. You'll tackle problems like handshake calculations using iterative and recursive methods, and learn to engineer date-time features for machine learning models. This section also includes an in-depth look at the IRIS dataset, enhancing your understanding of evaluation metrics like precision, recall, and AUC ROC.

Section 6: Python Libraries and Data Analysis

Gain proficiency in using Python’s standard libraries for mathematical operations, random number generation, and file handling. Explore exploratory data analysis techniques through practical examples, such as football play analysis, and understand how to manage and clean data effectively.

Section 7: Advanced Data Visualization Techniques

Learn advanced visualization techniques to interpret and present data insights. This section covers distribution plots, KDE plots, joint plots, and the identification of outliers. You’ll develop skills to create compelling visual representations of data, aiding in both analysis and storytelling.

Section 8: Model Building and Evaluation

Finally, apply your skills to build and evaluate machine learning models. Learn the process of data preprocessing, model training, and evaluation with a focus on logistic regression. Understand key evaluation metrics and how to interpret them to improve your model’s performance.

By the end of this course, you will have a robust understanding of Python programming and its application in generative art and design. You will be equipped with the skills to create innovative art, handle complex data, and apply advanced techniques to real-world problems. Whether you aim to pursue a career in data science, art, or design, this course will provide the foundational and advanced skills needed to excel in these fields.

Prerequisites

none